mlsanityData to 14 Oct 2026

Identifying and Measuring Token-Level Sentiment Bias in Pre-trained Language Models with Prompts

arXiv 2204.07289 ↗First posted 15 Apr 2022cs.CLcited by 4

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Abstract (arXiv)

Due to the superior performance, large-scale pre-trained language models (PLMs) have been widely adopted in many aspects of human society. However, we still lack effective tools to understand the potential bias embedded in the black-box models. Recent advances in prompt tuning show the possibility to explore the internal mechanism of the PLMs. In this work, we propose two token-level sentiment tests: Sentiment Association Test (SAT) and Sentiment Shift Test (SST) which utilize the prompt as a probe to detect the latent bias in the PLMs. Our experiments on the collection of sentiment datasets show that both SAT and SST can identify sentiment bias in PLMs and SST is able to quantify the bias. The results also suggest that fine-tuning can possibly augment the existing bias in PLMs.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 18 of its 33 references that are in the map. First posted on arXiv 15 Apr 2022.

Builds on

18 of 33 references.

References · 18

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  2. 2020Language Models are Few-Shot Learnerscited by 32,419
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  11. 2020Social Biases in NLP Models as Barriers for Persons with Disabilitiescited by 182
  12. 2019Reducing Sentiment Bias in Language Models via Counterfactual Evaluationcited by 148
6 more
  1. 2016Yelp Dataset Challenge: Review Rating Predictioncited by 84
  2. 2020The Radicalization Risks of GPT-3 and Advanced Neural Language Modelscited by 76
  3. 2020Investigating African-American Vernacular English in Transformer-Based Text Generationcited by 53
  4. 2020SqueezeBERT: What can computer vision teach NLP about efficient neural networks?cited by 50
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  6. 2020"You are grounded!": Latent Name Artifacts in Pre-trained Language Modelscited by 39

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Cited by year

Citing papers per year: 2023: 2; 2024: 1; 2025: 1.

4 papers.

  1. 2023Evaluation of African American Language Bias in Natural Language Generationcited by 36
  2. 2024Can LLMs Reason with Rules? Logic Scaffolding for Stress-Testing and Improving LLMscited by 28
  3. 2023Can LLMs facilitate interpretation of pre-trained language models?cited by 16
  4. 2025Structured Reasoning for Fairness: A Multi-Agent Approach to Bias Detection in Textual Datacited by 2